This study proposes a novel statistical methodology for addressing an important problem in secondary data analysis in the epidemiology of aging. The investigators propose to develop a method to combine data from two separate samples to model the relationship of a response y to a covariate a when y and a do not coexist in one sample. The general relationship of interest is wealth (u) and health (y). Specifically, primary data contain the response y of interest, a biological or clinical health measurement. The investigators use the third National Health and Nutrition Examination Survey (NHANES), which contains detailed data based on clinical examination. However, rather than financial status information, NHANES only contains standard sociodemographic proxy variables x which are loosely related to wealth. The relationship of these proxy, or surrogate, variables to the true financial status indicators a is to be modelled using an auxiliary external validation data set, the Health and Retirement Study. The resulting surrogate model will then be used in the primary data (NHANES) to study the relationship of interest. The problem is formalized as one of errors-in-covariates. The proposed study will investigate the feasibility and subsequently develop a method for addressing this problem. Additionally, the method will be demonstrated with a real substantive question which has received little attention in the US: Is there an association between bone mineral density (BMD) or the prevalence of osteopenia (low BMD) and wealth? The question is of interest because the geographic pattern of hip fractures among whites in the US suggests a possible association between pathology and lifelong poverty. Specific aims are: (1.) To develop and test a regression model for the mean and variance of a multivariate surrogate x as a function of the missing covariate u and other covariates w. (2.) To use the model developed in Aim 1 and a new methodology for errors-in-covariates to model the regression of health outcome y in terms of a and w.